GAMIV: a genetic algorithm for identifying variable-lengthmotifs in noncoding DNA

Authors:
David J. Gagne
Affiliations:
University of Southern Maine, Portland, ME, USA
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

Citing 3
Cited 0

The evolutionary computation approach to motif discovery in biological sequences

GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
An Evaluation of Information Content as a Metric for the Inference of Putative Conserved Noncoding Regions in DNA Sequences Using a Genetic Algorithms Approach

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MOGAMOD: Multi-objective genetic algorithm for motif discovery

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

GAMI uses a genetic algorithm to identify putatively conserved motifs of a pre-selected length in noncoding DNA from diverse species. In this work, I present an extension to the system, GAMIV, that identifies putatively conserved motifs of variable length. The system begins with an initial set of very short motifs and allows them to grow through a pair of custom operators. A fitness function that rewards both motif conservation and motif length is used to evolve a population of conserved motifs of variable length. This paper describes the motivation for GAMIV, discusses the design of the system, and presents initial results for the system. Based on these initial results, GAMIV is a promising tool for the inference of variable-length motifs in noncoding DNA.